Interdisciplinary Sciences: Computational Life Sciences最新文献

{"title":"stGNN: Spatially Informed Cell-Type Deconvolution Based on Deep Graph Learning and Statistical Modeling.","authors":"Juntong Zhu, Daoyuan Wang, Siqi Chen, Lili Meng, Yahui Long, Cheng Liang","doi":"10.1007/s12539-025-00728-0","DOIUrl":"https://doi.org/10.1007/s12539-025-00728-0","url":null,"abstract":"<p><p>Recent advancements in spatial transcriptomics (ST) technologies have greatly revolutionized our understanding of tissue heterogeneity and cellular functions. However, popular ST, such as 10x Visium, still fall short in achieving true single-cell resolution, underscoring an urgent need for in-silico methods that can accurately resolve cell type composition within ST data. While several methods have been proposed, most rely solely on gene expression profiles, often neglecting spatial context, which results in suboptimal performance. Additionally, many deconvolution methods dependent on scRNA-seq data fail to align the distribution of ST and scRNA-seq reference data, consequently affecting the accuracy of cell type mapping. In this study, we propose stGNN, a novel spatially-informed graph learning framework powered by statistical modeling for resolving fine-grained cell type compositions in ST. To capture comprehensive features, we develop a dual encoding module, utilizing both a graph convolutional network (GCN) and an auto-encoder to learn spatial and non-spatial representations respectively. Following that, we further design an adaptive attention mechanism to integrate these representations layer-by-layer, capturing multi-scale spatial structures from low to high order and thus improving representation learning. Additionally, for model training, we adopt a negative log-likelihood loss function that aligns the distribution of ST data with scRNA-seq (or snRNA-seq) reference data, enhancing the accuracy of cell type proportion prediction in ST. To assess the performance of stGNN, we applied our proposed model to six ST datasets from various platforms, including 10x Visium, Slide-seqV2, and Visium HD, for cell type proportion estimation. Our results demonstrate that stGNN consistently outperforms seven state-of-the-art methods. Notably, when applied to mouse brain tissues, stGNN successfully resolves clear cortical layers at a high resolution. Additionally, we show that stGNN is able to effectively resolve ST at different resolutions. In summary, stGNN provides a powerful framework for analyzing the spatial distribution of diverse cell populations in complex tissue structures. stGNN's code is openly shared on https://github.com/LiangSDNULab/stGNN .</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144505611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Multi-grade Brain Tumor Classification Using Adaptive Hierarchical Optimized Horse Herd BiLSTM Fusion Network in MRI Images.","authors":"T Thanya, T Jeslin","doi":"10.1007/s12539-025-00708-4","DOIUrl":"https://doi.org/10.1007/s12539-025-00708-4","url":null,"abstract":"<p><p>Brain tumor classification using Magnetic Resonance Imaging (MRI) images is an important and emerging field of medical imaging and artificial intelligence in the current world. With advancements in technology, particularly in deep learning and machine learning, researchers and clinicians are leveraging these tools to create complex models that, using MRI data, can reliably detect and classify tumors in the brain. However, it has a number of drawbacks, including the intricacy of tumor types and grades, intensity variations in MRI data and tumors varying in severity. This paper proposes a Multi-Grade Hierarchical Classification Network Model (MGHCN) for the hierarchical classification of tumor grades in MRI images. The model's distinctive feature lies in its ability to categorize tumors into multiple grades, thereby capturing the hierarchical nature of tumor severity. To address variations in intensity levels across different MRI samples, an Improved Adaptive Intensity Normalization (IAIN) pre-processing step is employed. This step standardizes intensity values, effectively mitigating the impact of intensity variations and ensuring more consistent analyses. The model renders utilization of the Dual Tree Complex Wavelet Transform with Enhanced Trigonometric Features (DTCWT-ETF) for efficient feature extraction. DTCWT-ETF captures both spatial and frequency characteristics, allowing the model to distinguish between different tumor types more effectively. In the classification stage, the framework introduces the Adaptive Hierarchical Optimized Horse Herd BiLSTM Fusion Network (AHOHH-BiLSTM). This multi-grade classification model is designed with a comprehensive architecture, including distinct layers that enhance the learning process and adaptively refine parameters. The purpose of this study is to improve the precision of distinguishing different grades of tumors in MRI images. To evaluate the proposed MGHCN framework, a set of evaluation metrics is incorporated which includes precision, recall, and the F1-score. The structure employs BraTS Challenge 2021, Br35H, and BraTS Challenge 2023 datasets, a significant combination that ensures comprehensive training and evaluation. The MGHCN framework aims to enhance brain tumor classification in MRI images by utilizing these datasets along with a comprehensive set of evaluation metrics, providing a more thorough and sophisticated understanding of its capabilities and performance.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144325602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MOPSOGAT: Predicting CircRNA-Disease Associations via Improved Multi-objective Particle Swarm Optimization and Graph Attention Network.","authors":"Yuehao Wang, Pengli Lu","doi":"10.1007/s12539-025-00725-3","DOIUrl":"https://doi.org/10.1007/s12539-025-00725-3","url":null,"abstract":"<p><p>Recently increasing researches have discovered that circRNAs are remarkably reliable in organisms and play a crucial role as marker in many diseases. Although deep learning techniques has been universally applied to investigate the relationship of circRNA-disease, optimizing many parameters involved in these techniques for best performance has been a challenge. Therefore, we present, for the first time, a multi-objective particle swarm optimization algorithm to optimize the parameters in a graph attention network, ensuring that the model operates at peak efficiency. In addition, it also limits feature learning due to uneven distribution of different node types in heterogeneous graphs based on association relationships. We suggest a unique approach, MOPSOGAT, to overcome the aforementioned problems. MOPSOGAT is a method for predicting circRNA-disease associations utilizing the improved multi-objective particle swarm optimization (MOPSO) and the graph attention network. Initially, we obtain node sequences by utilizing multiple circRNA similarities and disease phenotypic similarities, and employing a heterogeneous graph with random walks incorporating jump and stay strategies. These sequences are then processed using word2vec to derive the neighbor vectors of the nodes, thus providing initial embeddings for circRNAs and diseases. Subsequently, in order to model convergence and diversity of the Pareto front solutions, an improved MOPSO algorithm is used to iteratively search for optimal solutions in the parameter space. After MOPSO optimization, parameters are fed into a graph attention network to further refine the model embedding. As a result, MOPSOGAT performs better than deep learning based methods, solely multi-objective optimization-based methods and machine learning-based ways. Moreover, the potential associations predicted by MOPSOGAT have been validated through case studies, further demonstrating the potential of MOPSOGAT in future biomedical research.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144293713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DCPPS: Prediction of Kinase-Specific Phosphorylation Sites Using Dynamic Embedding and Cross-Representation Interaction.","authors":"Mengya Liu, Xin Wang, Zhan-Li Sun, Xiao Yang, Xia Chen","doi":"10.1007/s12539-025-00731-5","DOIUrl":"https://doi.org/10.1007/s12539-025-00731-5","url":null,"abstract":"<p><p>Substrate-specific kinases catalyze addition of phosphate groups to specific amino acids, resulting in kinase-specific phosphorylation. It participates in various signaling pathways and regulation processes. The relevant computational methods can accelerate study of protein function research, disease exploration, and drug development. Existing approaches typically rely on global and local sequences to extract predictive features but often neglect position information and critical feature interaction, which is essential for effective feature representation. In this work, we propose a novel kinase-specific phosphorylation site prediction model, DCPPS, by leveraging dynamic embedding encoding and interaction between global and local representations. Specifically, to enrich sequence position information and strengthen features, we construct a dynamic embedding encoding (DEE) to capture amino acid semantics and positional information of upstream and downstream amino acids, dynamically optimizing feature embeddings. Considering the lack of in-depth feature interaction between local and global information, we design a cross-representation interaction unit (CRIU) to facilitate in-depth mining and complementary improvement of potential connections between multi-source features. Results of kinase-specific phosphorylation and multiple extended experiments show that DCPPS has better predictive performance and scalability. Further ablation studies demonstrate that incorporating global protein information, DEE, and CRIU markedly enhances phosphorylation site prediction accuracy, particularly in mitigating class imbalance.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144274753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"OptimDase: An Algorithm for Predicting DNA Binding Sites with Combined Feature Encoding.","authors":"Zhendong Liu, Jun S Liu, Dongqing Wei, Rongjun Man, Jiamin Jiang, Bofeng Zhang, Liping Li, Zhiyong Zhao","doi":"10.1007/s12539-025-00704-8","DOIUrl":"https://doi.org/10.1007/s12539-025-00704-8","url":null,"abstract":"<p><p>Identifying DNA binding sites remains a critical task in bioinformatics, with applications ranging from gene regulation studies to drug design. Although progress has been made in computational techniques, we still face challenges such as data complexity and prediction accuracy. In this paper, we introduce OptimDase, a new algorithm. It integrates feature encoding with optimum decision-making frameworks to improve DNA binding site prediction. OptimDase integrates multi-scale scanning and feature selection strategies, making it highly effective for both classification and regression tasks. Our experiments demonstrate that OptimDase achieves superior performance with an accuracy of 0.8943 in classification tasks and an RMSE of 0.0054 in regression tasks, outperforming existing algorithms in key evaluation metrics. These results highlight OptimDase's portability and robustness, making it an effective solution for identifying DNA binding sites and advancing the applications of drug design.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144266102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CPE-Pro: A Structure-Sensitive Deep Learning Method for Protein Representation and Origin Evaluation.","authors":"Wenrui Gou, Wenhui Ge, Yang Tan, Mingchen Li, Guisheng Fan, Huiqun Yu","doi":"10.1007/s12539-025-00732-4","DOIUrl":"https://doi.org/10.1007/s12539-025-00732-4","url":null,"abstract":"<p><p>Protein structures are fundamental to understanding their functions and interactions. With the continuous advancement of protein structure prediction methods, structure databases are rapidly expanding. Identifying the origin of protein structures is crucial for assessing the reliability of experimental resolution and computational prediction methods, as well as for guiding downstream biological research. Existing protein representation approaches often fail to capture subtle yet critical structural differences, posing challenges for precise structural traceability. To address this, we propose a structure-sensitive supervised deep learning model, Crystal vs Predicted Evaluator for Protein Structure (CPE-Pro), for the representation and origin evaluation of protein structures. CPE-Pro integrates a pre-trained protein Structural Sequence Language Model (SSLM) and Geometric Vector Perceptron-Graph Neural Network (GVP-GNN) to learn structure-aware protein representations and capture structural differences, enabling accurate classification across four origins of structural data. Preliminary results indicate that, compared to large-scale protein language models trained on extensive amino acid sequences, structural sequences enriched with local structural features enable the model to capture more informative protein characteristics, thereby enhancing and refining protein representations. Future research directions include extending the architecture to additional protein structure paradigms and developing evaluation methodologies for low-pLDDT predicted structures, providing more effective tools for protein structure analysis. The code, model weights, and all relevant materials are available at https://github.com/wr1102/CPE-Pro .</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144247736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DynamicDTA: Drug-Target Binding Affinity Prediction Using Dynamic Descriptors and Graph Representation.","authors":"Dan Luo, Jinyu Zhou, Le Xu, Sisi Yuan, Xuan Lin","doi":"10.1007/s12539-025-00729-z","DOIUrl":"https://doi.org/10.1007/s12539-025-00729-z","url":null,"abstract":"<p><strong>Motivation: </strong>Predicting drug-target binding affinity (DTA) is essential for identifying potential therapeutic candidates in drug discovery. However, most existing models rely heavily on static protein structures, often overlooking the dynamic nature of proteins, which is crucial for capturing conformational flexibility that will be beneficial for protein binding interactions.</p><p><strong>Methods: </strong>We introduce DynamicDTA, an innovative deep learning framework that incorporates static and dynamic protein features to enhance DTA prediction. The proposed DynamicDTA takes three types of inputs, including drug sequence, protein sequence, and dynamic descriptors. A molecular graph representation of the drug sequence is generated and subsequently processed through graph convolutional network, while the protein sequence is encoded using dilated convolutions. Dynamic descriptors, such as root mean square fluctuation, are processed through a multi-layer perceptron. These embedding features are fused with static protein features using cross-attention, and a tensor fusion network integrates all three modalities for DTA prediction.</p><p><strong>Results: </strong>Extensive experiments on three datasets demonstrate that DynamicDTA achieves by at least 3.4% improvement in <math><msub><mi>e</mi> <mtext>RMSE</mtext></msub> </math> score with comparison to seven state-of-the-art baseline methods. Additionally, predicting novel drugs for Human Immunodeficiency Virus Type 1 and visualizing the docking complexes further demonstrates the reliability and biological relevance of DynamicDTA.</p><p><strong>Availability and implementation: </strong>The source code is publicly available and can be accessed at https://github.com/shmily-ld/DynamicDTA .</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144247737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Brain Tumor Classification and Grading Using Multi-scale Graph Neural Network with Spatio-Temporal Transformer Attention Through MRI Scans.","authors":"Somya Srivastava, Parita Jain, Sanjay Kr Pandey, Gaurav Dubey, Nripendra Narayan Das","doi":"10.1007/s12539-025-00718-2","DOIUrl":"https://doi.org/10.1007/s12539-025-00718-2","url":null,"abstract":"<p><p>The medical field uses Magnetic Resonance Imaging (MRI) as an essential diagnostic tool which provides doctors non-invasive images of brain structures and pathological conditions. Brain tumor detection stands as a vital application that needs specific and effective approaches for both medical diagnosis and treatment procedures. The challenges from manual examination of MRI scans stem from inconsistent tumor features including heterogeneity and irregular dimensions which results in inaccurate assessments of tumor size. To address these challenges, this paper proposes an Automated Classification and Grading Diagnosis Model (ACGDM) using MRI images. Unlike conventional methods, ACGDM introduces a Multi-Scale Graph Neural Network (MSGNN), which dynamically captures hierarchical and multi-scale dependencies in MRI data, enabling more accurate feature representation and contextual analysis. Additionally, the Spatio-Temporal Transformer Attention Mechanism (STTAM) effectively models both spatial MRI patterns and temporal evolution by incorporating cross-frame dependencies, enhancing the model's sensitivity to subtle disease progression. By analyzing multi-modal MRI sequences, ACGDM dynamically adjusts its focus across spatial and temporal dimensions, enabling precise identification of salient features. Simulations are conducted using Python and standard libraries to evaluate the model on the BRATS 2018, 2019, 2020 datasets and the Br235H dataset, encompassing diverse MRI scans with expert annotations. Extensive experimentation demonstrates 99.8% accuracy in detecting various tumor types, showcasing its potential to revolutionize diagnostic practices and improve patient outcomes.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144225370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multi-Task Deep Learning Approach for Simultaneous Sleep Staging and Apnea Detection for Elderly People.","authors":"Lei Shi, Ranran Gui, Li Wang, Peng Li, Qunfeng Niu","doi":"10.1007/s12539-025-00721-7","DOIUrl":"https://doi.org/10.1007/s12539-025-00721-7","url":null,"abstract":"","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144234005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CMedRAGBot: A Chinese Medical Chatbot Based on Graph RAG and Large Language Models.","authors":"Dongfang Zhang, Haoze Du, Xiaolei Wang, Mingdong Zhu, Xiaoxiao Pang, Dongqing Wei, Xianfang Wang","doi":"10.1007/s12539-025-00715-5","DOIUrl":"https://doi.org/10.1007/s12539-025-00715-5","url":null,"abstract":"<p><p>In the domain of Chinese clinical medical question-answering (QA), traditional Large Language Models (LLMs) encounter challenges such as hallucinations and difficulties in updating knowledge for knowledge-intensive tasks. To address these issues, this research presents a Chinese clinical medical QA model that integrates Retrieval-Augmented Generation (RAG) and a medical knowledge graph, named CMedRAGBot. First, a Chinese medical knowledge graph encompassing multiple entity types-including diseases, medications, and symptoms-is constructed. Based on this knowledge graph, a Named Entity Recognition (NER) model built on a Chinese-RoBERTa and BiGRU architecture is designed, with data augmentation strategies employed to enhance its generalization capability. In addition, prompt engineering techniques are used to implement intent recognition for user queries, mapping them to predefined intent categories. Finally, the aforementioned modules are integrated to form a complete Chinese clinical medical QA system. In the experimental evaluation, CMedRAGBot is deployed on five state-of-the-art LLMs (including ChatGPT-4o, ChatGPT-o1, DeepSeek-R1, Llama-3.3-70B-Instruct, and Gemini 2.0 Flash) and tested using specialized question banks derived from the Chinese Clinical Medical Qualification Examinations and Residency Standardization Training Examinations from 2000 to 2023. The results indicate that the integration of CMedRAGBot significantly improves the test accuracy of all models, with increases of up to approximately 10%. Furthermore, ablation experiments reveal that data augmentation enhances NER model's F1 score from 95.27% to 97.55%, while the inclusion of an intent recognition module markedly improves the model's ability to understand complex queries, thereby further boosting answer accuracy. Source code of the research is available at https://github.com/zhdongfang/CMedRAGBot .</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144234006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}